In the field of computer assisted surgery, it is often required to track a structure in a human body, such as a bone or an organ. In particular, in computer assisted orthopaedic surgery, the motion of bones is tracked with a three-dimensional (3D) position measurement system. This is typically carried out by attaching a marker to the bone invasively, by drilling pins or screws into the bone, creating holes and causing trauma to the tissue and structure. This can increase the risk of bone fracture, infection, and cause pain to the patient. Some examples of these intra-operative motion tracking systems can are described to United States Patent Application publication No. 20060250300 entitled ‘RF system for tracking objects’ and in United States Patent Application Publication No. 20050245821 entitled “Position Sensing System for Orthopedic Applications”, each of which is hereby incorporated by reference in its entirety.
Furthermore, such systems are not suitable for measuring the motion of a subject or patient outside of the operating room, when the patient is not under anesthesia. This is due to the invasiveness of current tracking systems, and the abovementioned factors. Normally, in the analysis of human movement, such, as in gait analysis, the motion of the underlying bones is inferred by tracking the motion of the overlying skin. This is typically carried oat by attaching markers to the akin using adhesive means, or straps, or by attaching markers to fitted clothes on the subject. While not invasive, this method has the disadvantage of being less accurate, because of the motion of the skin and other overlying soft-tissues such as muscle with respect to the bone surface.
Other methods for measuring in-vivo bone kinematics use live 2D projected fluoroscopy images and intensity-based three-dimensional to two-dimensional image registration techniques (see for example the article by Komistek et. al. entitled In Vivo fluoroscopic Analysis of the Normal Human Knee, in CLINICAL ORTHOPAEDICS AND RELATED RESEARCH, Number 410, pp. 69-81, 2003). Komistek's method requires the construction of three-dimensional computer-aided design models from pre-operative segmented computed tomography (CT) or magnetic resonance imaging (MRI) scans, and to register these models to 2D fluoroscopic images using an optimization algorithm that automatically adjusts the pose of the model at various knee flexion angles to best match the anatomy on the projected live images. Disadvantages of such techniques are that large and expensive imaging apparatuses are required, and that they expose the patient to ionizing radiation. Moreover, these systems are not suitable for use in most surgical settings duo to their size and complexity.
In the article entitled ‘A system for ultrasound-guided computer-assisted orthopaedic surgery’ by Chen et al. in Computer Aided Surgery, September/November 2005; 10(5/6): 281-292, a method for non-invasive localizing a bone of a patient using two-dimensional (ie B-mode) ultrasound (US) is presented, Chen's method includes the following points:                Preoperatively, a set of 2D freehand US images (e.g., a total of 2000 images) is acquired from tire targeted anatomy along with their corresponding positional formation on the US probe. These preoperative image data are used to construct a preoperative database that serves two main purposes:                    to construct a preoperative 3D volumetric representation of the patient's anatomy that can be used for surgical planning (stage no. 1 in FIG. 2),            to form a preoperative searchable image data-base for use by the registration process.                        Intraoperatively, the preoperative US volume is registered to the patient using intraoperative 2D US images.                    In the OR, the surgeon takes a few live OS images of the targeted anatomy while the position of the US probe is tracked in real time by the camera system. These intraoperative US images are used to find the physical position of the patient during the surgery (see the lower left image in FIG. 2).            A mutual information-based registration algorithm is employed to find the closest match to the live image in the preoperative image database (stage no. 2 in FIG. 2).            It should be borne in mind that the same images searched for in the preoperative database are also the ones used to construct the preoperative US volume of the targeted anatomy. Assuming the closest matching image is actually the live image, we can register the preoperative 3D US volume to the live US image (the lower right image in FIG. 2) and thus to the patient for surgical guidance (stage no. 3 in FIG. 2).                        
In other words, Chen's method involves constructing a huge database of a couple thousand localized 2D ultrasound images preoperatively, and comparing each one of these images (or a reduced subset thereof) to an intra-operative localized ‘live’ 2D image of the bone. If there is a good match between one of the 2D images in fee database and the live 2D image, it is assumed that the live image was acquired in the same plane as the localized one in the database. Therefore, a fundamental requirement of Chen's method to accurately track the bone is that there must be a 2D image in the so-called preoperative database that has been acquired in the same acquisition plane that the intra-operative US image has been acquired in, otherwise the matching algorithm cannot accurately determine the location of the bone. Another drawback is that any patient motion occurring during the pre-operative acquisition of 2000 or so images will result in relative errors between the ore-op image slices in the database (i.e. volumetric errors in the preoperative 3D US Volume).